A Hybrid Heuristic Algorithm for the Clustered Traveling Salesman Problem

Mestria, Mário

doi:10.1590/0101-7438.2016.036.01.0113

Cited by 15 publications

(18 citation statements)

References 34 publications

(39 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Among the six proposed heuristics, one heuristic corresponds to the traditional GRASP procedure whereas the other heuristics include different path relinking procedures. In [24], Mestria studied a hybrid heuristic, which is based on a combination of GRASP, Iterated Local Search (ILS) and Variable Neighborhood Descent (VND). Recently, Mestria [25] presented another complex hybrid algorithm (VNRDGILS) which mixes GRASP, ILS, and Variable Neighborhood Random Descent to explore several neighborhoods.…”

Section: Literature Review On Existing Solution Methodsmentioning

confidence: 99%

Solving the Clustered Traveling Salesman Problem via TSP methods

Hao

Wu³

2020

Preprint

View full text Add to dashboard Cite

The Clustered Traveling Salesman Problem (CTSP) is a variant of the popular Traveling Salesman Problem (TSP) arising from a number of real-life applications. In this work, we explore an uncharted solution approach that solves the CTSP by transforming it to the well-studied TSP. For this purpose, we first investigate a technique to convert a CTSP instance to a TSP and then apply popular TSP solvers (including exact and heuristic solvers) to solve the resulting TSP instance.We want to answer the following questions: How do state-of-the-art TSP solvers perform on clustered instances converted from the CTSP? Do state-of-the-art TSP solvers compete well with the best performing methods specifically designed for the CTSP? For this purpose, we present intensive computational experiments on various CTSP benchmark instances to draw conclusions.

show abstract

Section: Literature Review On Existing Solution Methodsmentioning

confidence: 99%

Solving the Clustered Traveling Salesman Problem via TSP methods

Hao

Wu³

2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Then, whether this cycle is shorter than the shortest one that has been found so far (line 17) is checked. If this proves true, the current cycle is stored as the shortest one (lines [19][20][21][22][23][24]. The complexity of the algorithm is exponential because it generates all (VertexCount-1)!…”

Section: Methodsmentioning

confidence: 99%

An Analysis of a Recursive and an Iterative Algorithm for Generating Permutations Modified for Travelling Salesman Problem

Kralev

2017

International Journal on Advanced Science, Engineering and Information Technology

View full text Add to dashboard Cite

This paper presents the results of a comparative analysis between a recursive and an iterative algorithm when generating permutation. A number of studies discussing the problem and some methods dealing with its solution are analyzed. Recursion and iteration are approaches used in computer programs to implement different algorithms. An iterative approach is the repeated execution of the same source code until a certain end condition is met. On the other hand, a recursive approach uses a recursive function that repeatedly calls itself. This function contains a source code that must be executed repeatedly. Both algorithms presented in this paper can be used to generate permutations of an n element set. The algorithms are modified so that they can be used to solve the Travelling Salesman Problem (TSP) with a small number of vertices. Several publications that discuss the TSP and some approaches to its solution are also presented. The methodology and the conditions for conducting the experiments are described in details. The obtained results have been analyzed; they show that for the same conditions the iterative algorithm works from of 2 3 to 2 4 times faster than the recursive algorithm in all the tested input data. Several approaches to optimize the two algorithms in terms of the number of permutations tested when searching a minimal Hamiltonian cycle are presented.

show abstract

“…Mestria integrated the greedy randomized adaptive search procedure (GRASP), iterated local search, and variable neighborhood descent, proposing a hybrid method for clustered TSP. The hybrid heuristic outperformed several existing methods in terms of optimal solution and reasonable computational time for medium and large size instances [17].…”

Section: Literature Reviewmentioning

confidence: 99%

Greedy Mechanism Based Particle Swarm Optimization for Path Planning Problem of an Unmanned Surface Vehicle

Xin

Zhong

et al. 2019

Sensors

View full text Add to dashboard Cite

Recently, issues of climate change, environment abnormality, individual requirements, and national defense have caused extensive attention to the commercial, scientific, and military development of unmanned surface vehicles (USVs). In order to design high-quality routes for a multi-sensor integrated USV, this work improves the conventional particle swarm optimization algorithm by introducing the greedy mechanism and the 2-opt operation, based on a combination strategy. First, a greedy black box is established for particle initialization, overcoming the randomness of the conventional method and excluding a great number of infeasible solutions. Then the greedy selection strategy and 2-opt operation are adopted together for local searches, to maintain population diversity and eliminate path crossovers. In addition, Monte-Carlo simulations of eight instances are conducted to compare the improved algorithm with other existing algorithms. The computation results indicate that the improved algorithm has the superior performance, with the shortest route and satisfactory robustness, although a fraction of computing efficiency becomes sacrificed. Moreover, the effectiveness and reliability of the improved method is also verified by its multi-sensor-based application to a USV model in real marine environments.

show abstract

A Hybrid Heuristic Algorithm for the Clustered Traveling Salesman Problem

Cited by 15 publications

References 34 publications

Solving the Clustered Traveling Salesman Problem via TSP methods

Solving the Clustered Traveling Salesman Problem via TSP methods

An Analysis of a Recursive and an Iterative Algorithm for Generating Permutations Modified for Travelling Salesman Problem

Greedy Mechanism Based Particle Swarm Optimization for Path Planning Problem of an Unmanned Surface Vehicle

Contact Info

Product

Resources

About